research
∙
05/15/2023
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs
We derive a new analysis of Follow The Regularized Leader (FTRL) for onl...
share
research
∙
05/13/2023
Delay-Adapted Policy Optimization and Improved Regret for Adversarial MDP with Delayed Bandit Feedback
Policy Optimization (PO) is one of the most popular methods in Reinforce...
research
∙
02/07/2022
Policy Optimization for Stochastic Shortest Path
Policy optimization is among the most popular and successful reinforceme...
research
∙
01/31/2022
Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback
The standard assumption in reinforcement learning (RL) is that agents ob...
research
∙
01/31/2022
Cooperative Online Learning in Stochastic and Adversarial MDPs
We study cooperative online learning in stochastic and adversarial Marko...
research
∙
01/28/2022
Planning and Learning with Adaptive Lookahead
The classical Policy Iteration (PI) algorithm alternates between greedy ...
research
∙
03/24/2021
Minimax Regret for Stochastic Shortest Path
We study the Stochastic Shortest Path (SSP) problem in which an agent ha...
research
∙
12/29/2020
Learning Adversarial Markov Decision Processes with Delayed Feedback
Reinforcement learning typically assumes that the agent observes feedbac...
research
∙
09/13/2020
Oracle-Efficient Reinforcement Learning in Factored MDPs with Unknown Structure
We consider provably-efficient reinforcement learning (RL) in non-episod...
research
∙
06/20/2020
Adversarial Stochastic Shortest Path
Stochastic shortest path (SSP) is a well-known problem in planning and c...
research
∙
02/23/2020
Near-optimal Regret Bounds for Stochastic Shortest Path
Stochastic shortest path (SSP) is a well-known problem in planning and c...
research
∙
02/19/2020
Optimistic Policy Optimization with Bandit Feedback
Policy optimization methods are one of the most widely used classes of R...
research
∙
05/19/2019
